Data-Driven Mining of a Thermostable Lipase with Molecular Insights into Mechanisms of Its Substrate Specificity

Abstract

The discovery of novel heat-resistant lipases has the potential to broaden their applications in the food industry and other fields. This study identified a novel heat-resistant lipase PFHL from Pseudomonas fluorescens HK44 based on data-driven mining, which was subsequently expressed in Escherichia coli for its molecular insights into substrate specificity. The purified PFHL demonstrated optimal activity at 60 °C and pH 7.0 with a half-life of 2.52 h and retained 48.23% of its relative activity at 90 °C. The enzyme exhibited a substrate preference for medium-chain fatty acid esters, displaying a catalytic efficiency k_cat/K_m of 134.31 mM^–1·min^–1 toward 4-nitrophenyl laurate (pNPC₁₂), 1.45-fold higher than that for 4-nitrophenyl palmitate (pNPC₁₆). Additionally, molecular docking and molecular dynamics simulations were performed to elucidate the mechanism underlying its substrate specificity and catalytic efficiency. The results revealed that pNPC₁₂ fit the catalytic pocket better than pNPC₁₆, and the complex exhibited greater stability during the simulation. Up to seven hydrogen bonds were formed, and the substrate was able to rapidly reach and position itself within the catalytic pocket. These findings offer valuable insights into PFHL’s substrate preferences, laying the groundwork for its rational design and further optimization to enhance its suitability for industrial applications in the future.